class_docstr: A class representing a mean scaling approach. Inputs are simply rescaled
  based on their mean.
  
  Parameters
  ----------
  minimum_scale
      The minimum value to which re-scaled values will be clipped to.
      (default: 1e-10)
  clip_max
      The maximum value to which re-scaled values will be clipped to.
      Negative values will be clipped at -clip_max and positive values at
      clip_max. (default: None)
code: def post_transform(
      self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor]
  ) -> Tensor:
      transf_samples = F.broadcast_mul(samples, scale)
      return transf_samples
  
file_path: gluonts/src/gluonts/mx/representation/mean_scaling.py
imports: from typing import List, Optional, Tuple
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .representation import Representation
label_desc: Use image node_id 4 for calling the MeanScaling obj's underlying member method code with example usage: obj.post_transform(F, samples, scale, rep_params) and returns: transf_samples